MétaCan
Menu
← Back to cohort
Record W3184628163 · doi:10.82308/40774

Synthesis and biological study of aminoglycoside derivatives to overcome bacterial resistance

2010· article· en· W3184628163 on OpenAlexfundno aff
Xuxu Yan

Bibliographic record

VenueeScholarship@McGill (McGill) · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsnot available
FundersMcMaster UniversityMcGill University
KeywordsAminoglycosideChemistryMicrobiologyBiologyAntibiotics

Abstract

fetched live from OpenAlex

Aminoglycosides are broad-spectrum antibiotics that target the A-site of bacterial 16S ribosomal RNA. Their use, however, is increasingly threatened by the rapid spread of resistance. One of the most common determinants of aminoglycoside resistance in bacteria is the expression of a class of enzymes known as aminoglycoside 6′-N-acetyltransferases (AAC(6′)s). These enzymes use acetylcoenzyme A (AcCoA) to acetylate most aminoglycosides at the 6′-NH2, thus decreasing their affinity for RNA and leading to bacterial resistance. One strategy pursued by the Auclair research group to overcome aminoglycoside resistance is to develop AAC(6′) inhibitors. Chapter 2 of this thesis describes enzymatic studies with the group's first generation of inhibitors, aminoglycoside-CoA bisubstrates and truncated analogs. The bisubstrates exhibited potent nanomolar competitive inhibition of the Enterococcus faecium isoform AAC(6′)-Ii and proved to be useful mechanistic and structural probes. They did not however show activity in cells. Enzymatic studies with truncated bisubstrates allowed extensive SAR studies and led to the discovery of a second generation of inhibitors, one of which is capable of blocking aminoglycoside resistance in cells expressing AAC(6′)-Ii. To improve the potency of this compound, a series of derivatives with various amide groups replacing the ester functionality were synthesized. It was hypothesized that this modification would increase potency and biological stability. To our surprise however these small changes had a negative impact on the interaction between the inhibitors and AAC(6′)-Ii. Chapter 3 describes our second approach to counter aminoglycoside resistance, which involved the synthesis of new aminoglycosides designed to be active against resistant bacterial strains. A series of neamine N-6′-acylated derivatives were designed to retain the hydrogen bonding ability of 6′-N to RNA, yet disrupt binding to AAC(6′)-Ii. Some of these compounds showed moderate but not clinically relevant activity in cells.Finally, the third approach elaborated in Chapter 4 is to design prodrugs with dual, resistance inhibition and antibacterial activities. The goal was to develop aminoglycoside N-6′ derivatives as prodrugs that were expected to be extended to bisubstrate analogs by the CoA biosynthetic enzymes in bacteria. The resulting bisubstrates were expected to not only block aminoglycoside resistance via AAC(6′) inhibition, but to also kill bacteria by blocking the fatty acid biosynthetic pathway. A small series of aminoglycoside derivatives was thus synthesized. Unexpectedly none of them showed direct antibacterial activity. Preliminary biological studies however suggest that even in the absence of in vitro AAC(6′) inhibition, these molecules can potentiate the activity of aminoglycosides against an aminoglycoside resistant strain. To our knowledge they are the first reported prodrugs able to block aminoglycoside resistance in cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.223
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)→Same topicProbiotics and Fermented Foods→French-language works237,207→